Clustering semantic relations for constructing and maintaining knowledge organization tools

We propose a comprehensive methodology for thesaurus construction and maintenance combining shallow NLP with a clustering algorithm and an information visualization interface. The resulting system TermWatch, extracts terms from a text collection, mines semantic relations between them using complementary linguistic approaches and clusters terms using these semantic relations. The clusters formed exhibit the different relations necessary to populate a thesaurus or an ontology: synonymy, generic/specific and relatedness. The clusters represent, for a given term, its closest neighbours in terms of semantic relations. The clusters are mapped onto a 2D using an integrated visualization tool. This could change the way in which information professionals (librarians and documentalists) undertake knowledge organization tasks. TermWatch can be useful either as a starting point for grasping the conceptual organization of knowledge in a huge text collection without having to read the texts, then actually serving as a suggestive tool for populating different hierarchies of a thesaurus or an ontology because its clusters are based on semantic relations